Bioinformatics-Driven Multi-Factorial Insight into α-Galactosidase Mutations.

Area of Science:

• Genetics
• Biochemistry
• Computational Biology

Background:

• Fabry disease is a rare genetic disorder caused by deficient alpha-galactosidase A (AGAL) activity, leading to toxic globotriaosylceramide (Gb3) accumulation.
• Missense variants in the GLA gene cause Fabry disease, but interpreting their functional impact is difficult due to limited experimental data.

Purpose of the Study:

• To develop and apply an integrative computational framework for interpreting missense variants in the GLA gene.
• To identify molecular features distinguishing variants amenable to pharmacological chaperone therapy from non-amenable ones.

Main Methods:

• Integrated computational framework combining structural, interaction, pathogenicity, and stability data from in silico tools (AlphaMissense, EVE, FoldX) and experimental sources.
• Expert curation and structural analysis, focusing on variants amenable to pharmacological chaperones.
• Comparative analysis of different prediction models and investigation of outlier variants.

Main Results:

• Amenable GLA variants tend to preserve protein stability, whereas non-amenable variants are associated with structural destabilization.
• The study identified key molecular features differentiating amenable from non-amenable variants.
• Outlier variants, where predictions diverged from clinical data, were highlighted for further experimental validation.

Conclusions:

• The integrative computational framework improves missense variant interpretation in rare genetic disorders like Fabry disease.
• Understanding variant structural characteristics is crucial for predicting amenability to pharmacological chaperone therapy.
• This approach supports precision medicine by enabling better variant classification and guiding experimental validation.